Deep Learning based Segmentation of Optical Coherence Tomographic Images of Human Saphenous Varicose Vein

Maryam Viqar; Violeta Madjarova; Amit Kumar Yadav; Desislava Pashkuleva; Alexander S. Machikhin

doi:10.1364/DH.2022.W2A.5

Digital Holography and 3-D Imaging 2022
Technical Digest Series (Optica Publishing Group, 2022),
paper W2A.5
•https://doi.org/10.1364/DH.2022.W2A.5

Deep Learning based Segmentation of Optical Coherence Tomographic Images of Human Saphenous Varicose Vein

Maryam Viqar, Violeta Madjarova, Amit Kumar Yadav, Desislava Pashkuleva, and Alexander S. Machikhin

Digital Holography and Three-Dimensional Imaging 2022

Cambridge United Kingdom
1–4 August 2022
ISBN: 978-1-957171-12-8

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Joint Poster Session I (W2A)

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M. Viqar, V. Madjarova, A. K. Yadav, D. Pashkuleva, and A. S. Machikhin, "Deep Learning based Segmentation of Optical Coherence Tomographic Images of Human Saphenous Varicose Vein," in Digital Holography and 3-D Imaging 2022, D. Chu, J. Park, C. Cheng, and P. Ferraro, eds., Technical Digest Series (Optica Publishing Group, 2022), paper W2A.5.

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Abstract

Deep-learning based segmentation model is proposed for Optical Coherence Tomography images of human varicose vein based on the U-Net model employing atrous convolution with residual blocks, which gives an accuracy of 0.9932.

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